AIC average by age group

Run regressions between model parameters and age

## 
## Call:
## lm(formula = LL ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -157.706  -55.851    8.701   52.092  131.154 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -243.141     19.401 -12.532   <2e-16 ***
## age            2.813      1.149   2.448   0.0157 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 67.27 on 133 degrees of freedom
## Multiple R-squared:  0.04313,    Adjusted R-squared:  0.03593 
## F-statistic: 5.994 on 1 and 133 DF,  p-value: 0.01565
## 
## Call:
## lm(formula = alphaPosChoice ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.28765 -0.19218 -0.08989  0.12089  0.68734 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.863e-01  7.683e-02   3.727 0.000286 ***
## age         8.182e-05  4.551e-03   0.018 0.985681    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2664 on 133 degrees of freedom
## Multiple R-squared:  2.431e-06,  Adjusted R-squared:  -0.007516 
## F-statistic: 0.0003233 on 1 and 133 DF,  p-value: 0.9857
## 
## Call:
## lm(formula = alphaNegChoice ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.19615 -0.12346 -0.06146  0.00172  0.82184 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.29767    0.06518   4.567 1.12e-05 ***
## age         -0.01183    0.00386  -3.064  0.00264 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.226 on 133 degrees of freedom
## Multiple R-squared:  0.06595,    Adjusted R-squared:  0.05892 
## F-statistic:  9.39 on 1 and 133 DF,  p-value: 0.002642
## 
## Call:
## lm(formula = alphaPosComp ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.20486 -0.16290 -0.11220  0.01203  0.87932 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.261130   0.077306   3.378 0.000959 ***
## age         -0.006186   0.004579  -1.351 0.178941    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.268 on 133 degrees of freedom
## Multiple R-squared:  0.01354,    Adjusted R-squared:  0.006124 
## F-statistic: 1.826 on 1 and 133 DF,  p-value: 0.1789
## 
## Call:
## lm(formula = alphaNegComp ~ age, data = model_params)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.2291 -0.2075 -0.1851  0.1059  0.7982 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.253218   0.095197   2.660  0.00878 **
## age         -0.002721   0.005638  -0.483  0.63020   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3301 on 133 degrees of freedom
## Multiple R-squared:  0.001748,   Adjusted R-squared:  -0.005758 
## F-statistic: 0.2329 on 1 and 133 DF,  p-value: 0.6302
## 
## Call:
## lm(formula = betaAgency ~ age, data = model_params)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.553 -4.101 -1.937  3.475 19.945 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  3.91959    1.65918   2.362   0.0196 *
## age          0.24602    0.09827   2.504   0.0135 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.753 on 133 degrees of freedom
## Multiple R-squared:  0.04501,    Adjusted R-squared:  0.03783 
## F-statistic: 6.268 on 1 and 133 DF,  p-value: 0.0135
## 
## Call:
## lm(formula = betaMachine ~ age, data = model_params)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.060 -3.211 -1.095  1.786 22.739 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  4.89653    1.45762   3.359  0.00102 **
## age          0.15066    0.08633   1.745  0.08327 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.054 on 133 degrees of freedom
## Multiple R-squared:  0.02239,    Adjusted R-squared:  0.01504 
## F-statistic: 3.046 on 1 and 133 DF,  p-value: 0.08327
## 
## Call:
## lm(formula = agencyBonus ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.78412 -0.22038 -0.11946  0.06671  2.45157 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.663550   0.150879   4.398 2.22e-05 ***
## age         -0.014854   0.008936  -1.662   0.0988 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5231 on 133 degrees of freedom
## Multiple R-squared:  0.02035,    Adjusted R-squared:  0.01299 
## F-statistic: 2.763 on 1 and 133 DF,  p-value: 0.09882

Learning rate model

## Mixed Model Anova Table (Type 3 tests, S-method)
## 
## Model: estimate ~ age_z * valence * agency + (1 | subject_id)
## Data: learning_rates
##                 Effect        df         F p.value
## 1                age_z 1, 133.00    3.75 +    .055
## 2              valence 1, 399.00   8.68 **    .003
## 3               agency 1, 399.00      0.28    .596
## 4        age_z:valence 1, 399.00      0.89    .345
## 5         age_z:agency 1, 399.00      0.10    .751
## 6       valence:agency 1, 399.00 25.75 ***   <.001
## 7 age_z:valence:agency 1, 399.00    2.96 +    .086
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: estimate ~ age_z * valence * agency + (1 | subject_id)
##    Data: data
## 
## REML criterion at convergence: 180.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.4215 -0.6024 -0.3468  0.1215  3.2414 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_id (Intercept) 0.007237 0.08507 
##  Residual               0.068473 0.26167 
## Number of obs: 540, groups:  subject_id, 135
## 
## Fixed effects:
##                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)              0.191380   0.013432 133.000000  14.248  < 2e-16 ***
## age_z                   -0.026041   0.013444 133.000000  -1.937  0.05486 .  
## valence1                -0.033171   0.011261 399.000000  -2.946  0.00341 ** 
## agency1                  0.005975   0.011261 399.000000   0.531  0.59600    
## age_z:valence1          -0.010648   0.011271 399.000000  -0.945  0.34537    
## age_z:agency1           -0.003581   0.011271 399.000000  -0.318  0.75086    
## valence1:agency1        -0.057139   0.011261 399.000000  -5.074 5.97e-07 ***
## age_z:valence1:agency1  -0.019387   0.011271 399.000000  -1.720  0.08619 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) age_z valnc1 agncy1 ag_z:v1 ag_z:g1 vln1:1
## age_z       0.000                                            
## valence1    0.000  0.000                                     
## agency1     0.000  0.000 0.000                               
## age_z:vlnc1 0.000  0.000 0.000  0.000                        
## age_z:gncy1 0.000  0.000 0.000  0.000  0.000                 
## vlnc1:gncy1 0.000  0.000 0.000  0.000  0.000   0.000         
## ag_z:vln1:1 0.000  0.000 0.000  0.000  0.000   0.000   0.000
## 
##  Paired t-test
## 
## data:  model_params$alphaPosChoice and model_params$alphaNegChoice
## t = 6.4697, df = 134, p-value = 1.698e-09
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  0.1254031 0.2358366
## sample estimates:
## mean difference 
##       0.1806199
## 
##  Paired t-test
## 
## data:  model_params$alphaPosComp and model_params$alphaNegComp
## t = -1.1817, df = 134, p-value = 0.2394
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  -0.12816925  0.03229399
## sample estimates:
## mean difference 
##     -0.04793763

Plot relations between model parameters and age

---
title: "E2 VoC Analyses Part 3: RL Analyses"
date: 1/8/24
output:
    html_document:
        df_print: 'paged'
        toc: true
        toc_float:
            collapsed: false
            smooth_scroll: true
        number_sections: false
        code_download: true
        self_contained: true
---

```{r chunk settings, include = FALSE}
# set chunk settings
knitr::opts_chunk$set(echo = FALSE, 
                      cache = TRUE,
                      message = FALSE,
                      warning = FALSE)
knitr::opts_chunk$set(dpi=600)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
```

```{r load libraries, include = F}

#load libraries
library(tidyverse)
library(glue)
library(afex)
library(latex2exp)

#load scripts
source('analysis_scripts/voc_functions.R')
```

```{r import data}
# read in participant ages
participant_ages <- read_csv('data/voc_sub_info.csv') 

# read in learning data
learning_data <- read_csv('data/processed/learning_data.csv')

# exclude first-stage misses and first-stage RT < 150 ms
learning_data_filtered <- learning_data %>%
  filter(stage_1_rt > 150)

#exclude participants who made more than 300 of the same agency decisions
stage1_decisions <- learning_data_filtered %>%
  group_by(subject_id) %>%
  summarize(agency_choices = sum(stage_1_choice == 1)) %>%
  filter(agency_choices < 299) %>%
  filter(agency_choices > 15)

participant_ages <- participant_ages %>%
  filter(subject_id %in% stage1_decisions$subject_id)

## read in aics
aics_all = read_csv("RL_modeling/output/aics_all_16_models_100iter.csv") %>%
  rename(subject_id = subID)

# combine with ages
aics <- inner_join(aics_all, participant_ages, by = 'subject_id') %>%
  mutate(age_group = case_when(age < 13 ~ "Children",
                               age > 12.99 & age < 18 ~ "Adolescents",
                               age > 17.99 ~ "Adults"))

aics$age_group <- factor(aics$age_group, levels = c("Children", "Adolescents", "Adults"))
         

#pivot longer
model_results <- pivot_longer(aics, 
                      cols = oneAlpha_oneBeta:fourAlpha_twoBeta_agencyBonus,
                      names_to = "model",
                      values_to = "AIC")


model_results$model <- factor(model_results$model, 
                              levels = c("oneAlpha_oneBeta",
                                         "oneAlpha_twoBeta",
                                         "twoAlpha_oneBeta",
                                         "twoAlpha_twoBeta",
                                         "twoAlphaValenced_oneBeta",
                                         "twoAlphaValenced_twoBeta",
                                         "fourAlpha_oneBeta",
                                         "fourAlpha_twoBeta",
                                         "oneAlpha_oneBeta_agencyBonus",
                                         "oneAlpha_twoBeta_agencyBonus",
                                         "twoAlpha_oneBeta_agencyBonus",
                                         "twoAlpha_twoBeta_agencyBonus",
                                         "twoAlphaValenced_oneBeta_agencyBonus",
                                         "twoAlphaValenced_twoBeta_agencyBonus",
                                         "fourAlpha_oneBeta_agencyBonus",
                                         "fourAlpha_twoBeta_agencyBonus"))
model_results <- model_results %>%
  mutate(agencyBonus = case_when(str_detect(model, "agency") ~ "With Agency Bonus",
                                 !str_detect(model, "agency") ~ "No Agency Bonus"),
         shortName = str_remove(model, '_agencyBonus'))

model_results$shortName <- factor(model_results$shortName,
                                  levels = c("oneAlpha_oneBeta",
                                             "oneAlpha_twoBeta",
                                             "twoAlpha_oneBeta",
                                             "twoAlpha_twoBeta",
                                             "twoAlphaValenced_oneBeta",
                                             "twoAlphaValenced_twoBeta",
                                             "fourAlpha_oneBeta",
                                             "fourAlpha_twoBeta"))
```

#  AIC average by age group 
```{r plot AIC by age group, fig.width = 8, fig.height = 5, units = "in"}
#summarize
model_summary <- model_results %>%
  group_by(age_group, shortName, agencyBonus) %>%
  summarize(mean_aic = mean(AIC))

## Plot the results by age group 
AIC_age_plot <- ggplot(model_summary, aes(x = age_group, y = mean_aic, fill = shortName))+
  facet_wrap(~agencyBonus) +
  geom_bar(stat = "identity", position = "dodge", color = "black") +
  scale_fill_manual(name = "Model",
                    values = c(color8, color1, color2, color3, color4, color5, color6, color7, color1),
                    labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
  coord_cartesian(ylim = c(350, 650)) +
  ylab("Mean AIC") +
  xlab("") +
  voc_theme() +
  theme(axis.text.x = element_text(angle = 60, hjust = 1))
AIC_age_plot
```


#  Examine age-related change in parameter estimates from models
```{r load parameters from winning model}
model_params <- read_csv("RL_modeling/output/model_fits_real_data/fourAlpha_twoBeta_agencyBonus.csv",
                         col_names = c("negLL",
                                       "logPost",
                                       "AIC",
                                       "BIC",
                                       "alphaPosChoice",
                                       "alphaNegChoice",
                                       "alphaPosComp",
                                       "alphaNegComp",
                                       "betaAgency",
                                       "betaMachine",
                                       "agencyBonus")) 

#add sub ID and information
subject_id <- aics_all %>% select(subject_id) %>% unique()
model_params <- bind_cols(subject_id, model_params)
model_params <- inner_join(participant_ages, model_params, by = c("subject_id"))

```


# Run regressions between model parameters and age
```{r parameter regressions}
model_params$LL <- model_params$negLL * -1

# Log likelihood
summary(lm(LL ~ age, data = model_params))
# not significant

# Alpha Pos Choice
summary(lm(alphaPosChoice ~ age, data = model_params))
#not significant

# Alpha Neg Choice
summary(lm(alphaNegChoice ~ age, data = model_params))
# significant

# Alpha Pos Comp
summary(lm(alphaPosComp ~ age, data = model_params))
#not significant

# Alpha Neg Comp
summary(lm(alphaNegComp ~ age, data = model_params))
#not significant

# Beta Agency
summary(lm(betaAgency ~ age, data = model_params))
# significant

# Beta Bandit
summary(lm(betaMachine ~ age, data = model_params))
# not significant

# agency bonus
summary(lm(agencyBonus ~ age, data = model_params))
# not significant

```

# Learning rate model
```{r learning rate regression}
## Learning rate model
learning_rates <- model_params %>%
  pivot_longer(cols = c(alphaPosChoice:alphaNegComp),
               names_to = "learningRate",
               values_to = "estimate") %>%
  select(subject_id, age, learningRate, estimate) %>%
  unique() %>%
  mutate(valence = case_when(str_detect(learningRate, "Pos") ~ "Positive",
                             str_detect(learningRate, "Neg") ~ "Negative"),
         agency = case_when(str_detect(learningRate, "Choice") ~ "Choice",
                            str_detect(learningRate, "Comp") ~ "Comp"))

learning_rates$age_z <- scale_this(learning_rates$age)

learning_rate_model <- mixed(estimate ~ age_z * valence * agency + (1|subject_id),
                             data = learning_rates,
                             method = "S")
learning_rate_model
summary(learning_rate_model)
# main effect of age
# main effect of valence
# valence x agency interaction


#t test between alpha pos choice and alpha neg choice
t.test(model_params$alphaPosChoice, model_params$alphaNegChoice, paired = T)
#significant

#t test between alpha pos comp and alpha neg comp
t.test(model_params$alphaPosComp, model_params$alphaNegComp, paired = T)
#not significant

```


# Plot relations between model parameters and age
```{r age parameter plot, fig.width = 7, fig.height = 4, units = "in"}

params_long <- model_params %>%
  pivot_longer(names_to = "param",
               values_to = "estimate",
               cols = c(alphaPosChoice:agencyBonus)) 

params_long$param <- factor(params_long$param, 
                            levels = c("alphaPosChoice",
                                       "alphaNegChoice",
                                       "alphaPosComp",
                                       "alphaNegComp",
                                       "betaAgency",
                                       "betaMachine",
                                       "agencyBonus"),
                            labels = c(TeX("$\\alpha_{choice_+}$"), 
                                       TeX("$\\alpha_{choice_-}$"), 
                                       TeX("$\\alpha_{comp_+}$"), 
                                       TeX("$\\alpha_{comp_-}$"), 
                                       TeX("$\\beta_{agency}$"), 
                                       TeX("$\\beta_{machine}$"),
                                       "Agency~Bonus"
                            ))

params_plot <- ggplot(params_long, aes(x = age, y = estimate, color = param)) +
  facet_wrap(~param, scale = "free", labeller = label_parsed, nrow = 2) +
  geom_point() +
  geom_smooth(method = "lm", aes(fill = param)) +
  ylab("Parameter Estimate") +
  xlab("Age") +
  voc_theme() +
  theme(legend.position = "none")
params_plot
```


